Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/67670

Utilize Signal Traces from Others ? A Crowdsourcing Perspective of Energy Saving in Cellular Data Communication

Authors Ou, Zhonghong
Dong, Jiang
Dong, Shichao
Wu, Jun
Ylä-Jääski, Antti
Hui, Pan View this author's profile
Wang, Ren
Min, Alexander W.
Issue Date 2015
Source IEEE Transactions on Mobile Computing , v. 14, (1), January 2015, p. 194-207
Summary With the tremendous growth in wireless network deployment and increasing use of mobile devices, e.g., smartphones and tablets, improving energy efficiency in such devices, especially with communication driven workloads, is critical to providing a satisfactory user experience. Studies show that signal strength plays an important role on energy consumption of cellular data communications. While energy consumption can be minimized by accurately predicting signal strengths and reacting to it in real-time, the dynamic nature of wireless environments makes signal strengths highly unpredictable. In this paper, after analyzing in detail the signal strength variation and its impact on energy consumption, we propose to use crowdsourcing approach to optimize mobile devices' energy efficiency by utilizing signal strength traces reported/shared by other users/devices in cellular networks. Via a comprehensive measurement study, we observe that signal strength traces collected from different devices are pseudo-identical, and they even exhibit similar threshold-based behaviors in the relationship between signal strength and device power consumption. Based on our observations, we propose a predictive scheduling algorithm that: (i) selects the right set of signal strength traces based on its location, (ii) applies a filter to smooth out signal strengths and hide abrupt changes, (iii) digitizes the signal strength to "good" and "bad" areas, and (iv) schedules transmissions based on power-throughput characteristics to optimize the transmission energy efficiency. To demonstrate the efficacy of the proposed algorithms, we prototype the crowdsourcing-based predicative scheduling algorithm on Android-based smartphones. Our experiment results from real-life driving tests demonstrate that, by leveraging others' signal traces, mobile devices can save energy up to 35 percent compared to the conventional opportunistic scheduling, i.e., schedule transmissions only based on instantaneous channel conditions.
Subjects
ISSN 1536-1233
Language English
Format Article
Access View full-text via DOI
View full-text via Web of Science
View full-text via Scopus
Find@HKUST